Evolving Biological Behavior in Gene-Based Cellular Simulations

Cellular automata (CA) have long been capable of producing life-like behavior such as complexity, communication and self-replication using simple rules. Despite these properties, CA and other discrete simulations have failed to achieve real-world utility in cancer research or developmental biology, largely because they do not conform to a rules model which is understandable by clinicians and biologists. We present a method to generate CA with a desired phenotypic behavior within a biologically-based family of rule sets modeling simple gene regulation in a cell cycle signaling pathway. Designing CA within this biological context ensures the interpretability of any emergent results, thus opening the door for applications in biomedicine such as tumor growth and angiogenesis. Rule sets are encoded in intuitive genome structures, which are co-evolved using a Genetic Algorithm (GA) with a fitness function chosen to reward Wolfram's Type IV behavior. Results show the ability to generate interpretable type IV behavior in just a few hours on a desktop PC. This work is expected to have many applications including systems biology and cancer research.